ArticleBMC bioinformatics2022
Metacells untangle large and complex single-cell transcriptome networks.
Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
- Single-cell multi-omic profiling of heterogeneous PROCRiScience · 2026Article
- Hidden causal inference delineates dynamic lncRNA regulation in autism spectrum disorder.Briefings in bioinformatics · 2026Article
- Cell-type-specific m1A dynamics are associated with microglial phenotypic transition and neuronal metabolic adaptation during spinal cord injury.PLoS computational biology · 2026Article
- Predicting gene-specific regulation with transcriptomic and epigenetic single-cell data.Bioinformatics (Oxford, England) · 2026Article
- Single-cell transcriptomic analysis deciphers heterogeneity and transcriptional regulatory programs of sepsis with different prognosis.Biology direct · 2026Article
- Local control of dopamine release in nucleus accumbens gates opioid withdrawal aversion.bioRxiv : the preprint server for biology · 2026Article
- Linking Genetic Risk to Disease-Relevant Cellular States via Metacell-Informed Modeling with ICePop.bioRxiv : the preprint server for biology · 2026Article
- Multi-Network Co-expression Analysis Enhances Biological Insights from Single-Cell Gene Expression.Interdisciplinary sciences, computational life sciences · 2026Article
- mcRigor: A Statistical Software Package for Evaluating and Optimizing Metacell Partitioning in Single-Cell Data Analysis.Journal of computational biology : a journal of computational molecular cell biology · 2026Article
- Single-cell and spatial atlas unveil tumor-specific microenvironment convergence and a prognosis-associated PLXDC1+ myofibroblast population in metastatic bladder cancer.Journal of translational medicine · 2025Article
- Library size-stabilized metacells construction enhances co-expression network analysis in single-cell data.PLoS computational biology · 2025Article
- mcRigor: a statistical method to enhance the rigor of metacell partitioning in single-cell data analysis.Nature communications · 2025Article
- scMINER: a mutual information-based framework for clustering and hidden driver inference from single-cell transcriptomics data.Nature communications · 2025Article
- Latent epigenetic programs in Müller glia contribute to stress and disease response in the retina.Developmental cell · 2025Article
- New Multiomic Studies Shed Light on Cellular Diversity and Neuronal Susceptibility in Parkinson's Disease.Movement disorders : official journal of the Movement Disorder Society · 2025Review
- Integrated analysis of single-cell and bulk transcriptomics reveals cellular subtypes and molecular features associated with osteosarcoma prognosis.BMC cancer · 2025Article
- MetaQ: fast, scalable and accurate metacell inference via single-cell quantization.Nature communications · 2025Article
- Constructing Cell-Specific Causal Networks of Individual Cells for Depicting Dynamical Biological Processes.Research (Washington, D.C.) · 2025Article
- Augmenting the human interactome for disease prediction through gene networks inferred from human cell atlas.Animal cells and systems · 2025Article
- SuperSpot: coarse graining spatial transcriptomics data into metaspots.Bioinformatics (Oxford, England) · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
backgroundSingle-cell RNA sequencing (scRNA-seq) technologies offer unique opportunities for exploring heterogeneous cell populations. However, in-depth single-cell transcriptomic characterization of complex tissues often requires profiling tens to hundreds of thousands of cells. Such large numbers of cells represent an important hurdle for downstream analyses, interpretation and visualization.
resultsWe develop a framework called SuperCell to merge highly similar cells into metacells and perform standard scRNA-seq data analyses at the metacell level. Our systematic benchmarking demonstrates that metacells not only preserve but often improve the results of downstream analyses including visualization, clustering, differential expression, cell type annotation, gene correlation, imputation, RNA velocity and data integration. By capitalizing on the redundancy inherent to scRNA-seq data, metacells significantly facilitate and accelerate the construction and interpretation of single-cell atlases, as demonstrated by the integration of 1.46 million cells from COVID-19 patients in less than two hours on a standard desktop.
conclusionsSuperCell is a framework to build and analyze metacells in a way that efficiently preserves the results of scRNA-seq data analyses while significantly accelerating and facilitating them.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.